1. Field of the Invention
The invention is a computer-controlled machine vision system for inspecting pre-processed peanut pods for hull-scrape maturity class and profile. Other agricultural commodities such as seeds or kernels, could be inspected by this system, for example, for damage to the surface, for texture, and for color. Machine vision grading of agricultural commodities such as peanuts has greater accuracy grading than by human sight. Currently, pod ripeness is determined subjectively by human graders who compare pod ripeness color with a descriptive standard on a profile layout chart such as described in Williams et al, "A Non-Destructive Method for Determining Peanut Pod Maturity" (Peanut Science Vol. 8, 134-141, 1981.), incorporated herein by reference.
Additionally, greater precision in the grading, combined with the ability to evaluate samples more quickly provides minimally-trained personnel to perform a maturity grading task. In the absence of machine vision grading, less accurate shortcuts are often taken because of the large number of samples that must be graded in short periods of time. It is not uncommon for peanuts to gain about 300-500 pounds per acre in weight and 1 to 2% in grade in the week or week and a half before optimal harvest time. The increased gain in weight and grade can increase dollar per acre to the grower if the harvest time is accurately judged. Approximately 1.8 million acres of peanuts are produced in the United States each year. By the use of strategically placedmachine grading systems in peanut-producing counties, a much more highly reliable method of determining maturity of the peanut crop can be utilized.
2. Description of the Related Art
3-D vision systems for determining peanut pod maturity have been discussed in a paper entitled Optics in Agriculture, published in November of 1990 and incorporated herein by reference. Therein it was noted that pod ripeness color normally begins where the basal seed is attached to the hull. The colors gradually change from white to light-yellow to deep yellow to orange to brown to black with increasing maturity. These colors are subdivided in three to six classes, each for a total of 25 classes. Classes are based on the amount that one color has replaced another and represent approximately one-half week in the physiological age of the pod. Harvest decisions are primarily concerned with the last 13 of the classes that comprise the major ripeness colors. As in the use of humans in grading the maturity of the pods, it is necessary to correctly identify the advancing ripeness color in a machine vision system. The system described in the above-mentioned reference has drawbacks due to system cost and the complexity of providing like calibrations for the three cameras.